Work

A stitch recognition based on ResNet and an attention mechanism for Cantonese Embroidery

Year: 2026

Type: article

Abstract: Stitches are the defining feature of Cantonese Embroidery, an intangible cultural heritage of China. Their diversity and high flexibility pose significant challenges for automatic recognition. A new framework for the recognition of Cantonese Embroidery stitches in real-world embroidery scenarios is proposed. First, the stitch regions are segmented and extracted from embroidery images using SAM2, and a dataset is then established. Afterward, the Squeeze-and-Excitation module is refined and incorporated into a CNN structure, leading to an enhanced Squeeze-and-Excitation Residual Neural Network for Cantonese Embroidery (SE-ResNet-CE) stitch recognition model. Through this design, joint attention to global and local stitch features is achieved. The experimental results reveal that the proposed model achieves a classification accuracy of 98.81% and a Macro-F1 score of 98.3% on the dataset. This method effectively extracts fine-grained features of stitches, providing technical support for the digitization of embroidery.

Source: npj Heritage Science

DOI: 10.1038/s40494-026-02739-6